Isotop is a new neural method for nonlinear projection of high-dimensional data. Isotop builds the mapping between the data space and a projection space by means of topology preservation. Actually, the topology of the data to be projected is approximated by the use of neighborhoods between the neural units. Isotop is provided with a piecewise linear interpolator for the projection of generalization data after learning. Experiments on artificial and real data sets show the advantages of Isotop.